An enhanced minimum classification error learning framework for balancing insertion, deletion and substitution errors

In continuous speech recognition substitution, insertion and deletion errors usually not only vary in numbers but also have different degrees of impact on optimizing a set of acoustic models. To balance their contributions to the overall error, an enhanced minimum classification error (E-MCE) learning framework is developed. The basic idea is to partition acoustic model optimization into three subtasks, i.e., minimum substitution errors (MSE), insertion errors (MIE) and deletion errors (MDE), and select/generate three corresponding sets of competing hypotheses, one for each individual sub-problem. MSE, MIE and MDE are then sequentially executed to gradually reduce the overall word error rates. Experimental results on continuous Mandarin digit recognition of five different data sets collected over various acoustic conditions have consistently shown the effectiveness of the proposed E-MCE learning framework.